WPS4279
THE IMPACT OF CLIMATE CHANGE
ON LIVESTOCK MANAGEMENT IN AFRICA:
A STRUCTURAL RICARDIAN ANALYSIS1
Sungno Niggol Seo and Robert Mendelsohn2
World Bank Policy Research Working Paper 4279, July 2007
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
of ideas about development issues. An objective of the series is to get the findings out quickly, even if the
presentations are less than fully polished. The papers carry the names of the authors and should be cited
accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors.
They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they
represent. Policy Research Working Papers are available online at http://econ.worldbank.org.
1An earlier version of this Working Paper was published as CEEPA Discussion Paper number 23.
2University of Aberdeen Business School, United Kingdom and School of Forestry and Environmental Studies,
Yale University, 230 Prospect Street, New Haven, CT 06511, USA, Seo e-mail: niggol.seo@abdn.ac.uk;
Mendelsohn tel: 203-432-5128, e-mail: robert.mendelsohn@yale.edu.
The authors want especially to thank Pradeep Kurukulasuriya, Rashid Hassan, James Benhin, Ariel Dinar,
Temesgen Deressa, Mbaye Diop, Helmy Mohamed Eid, K Yerfi Fosu, Glwadys Gbetibouo, Suman Jain, Ali
Mahamadou, Reneth Mano, Jane Mariara, Samiha El-Marsafawy, Ernest Molua, Mathieu Ouedraogo, and Isidor
Sène.
This paper was funded by the GEF and the World Bank. It is part of a larger study on the effect of climate change on
agriculture in Africa, managed by the World Bank and coordinated by the Centre for Environmental Economics and
Policy in Africa (CEEPA), University of Pretoria, South Africa.
SUMMARY
This paper develops the structural Ricardian method, a new approach to modeling agricultural
performance using cross-sectional evidence, and uses the method to study animal husbandry in
Africa. The traditional Ricardian approach measures the interaction between climate and
agriculture (Mendelsohn et al. 1994; Seo et al. 2005) but it does not reveal how farmers actually
adapt. It is consequently difficult to compare traditional Ricardian results with microeconomic
models built from the details of agronomic research (e.g. Adams et al. 1990, 1999; Reilly et al.
1996). The Model is intended to estimate the structure beneath Ricardian results in order to
understand how farmers change their behavior in response to climate. In this African livestock
example, the Structural Ricardian Model estimates which species are selected, the number of
animals per farm, and the net revenue per animal. All three of these elements are climate
sensitive.
A three-equation model is developed to estimate each of the choices facing a farmer. For each
farm, a primary animal is defined as the species that is observed to earn the greatest net revenue
on that farm. A multinomial logit is then estimated to predict which primary animal each farmer
selects. Given the primary animal chosen, the second equation estimates the number of animals
of that type per farm. The final equation estimates the net revenue per animal by species.
The model is used to study the sensitivity of African animal husbandry decisions to climate. A
survey of over 5000 livestock farmers in ten countries reveals that the selection of species, the
net income per animal, and the number of animals are all highly dependent on climate. As
climate warms, net income across all animals will fall but especially across beef cattle. The fall
in net income causes African farmers to reduce the number of animals on their farms. The fall in
relative revenues also causes them to shift away from beef cattle and towards sheep and goats.
All farmers will lose income but the most vulnerable farms are large African farms that currently
specialize in beef cattle.
Small livestock and large livestock farms respond to climates differently. Small farms are
diversified, relying on dairy cattle, goats, sheep and chickens. Large farms specialize in dairy and
especially beef cattle. Estimating a separate multinomial logit selection model for small and large
farms reveals that the two types of farm choose species differently and specifically have different
climate response functions. The regressions of the number of animals also reveal that large farms
are more responsive to climate.
Several climate scenarios are tested using the estimated three-equation model. Some simple
uniform climate change scenarios are tested that assume a warming of 2.5°C or 5°C and a change
in precipitation of +15% or -15%. The purpose of these scenarios is to see how different districts
across Africa respond to identical changes in climate. Uniform warming causes the probability of
choosing beef cattle to fall where these are currently being chosen. In contrast, warming causes
the probability of choosing sheep to rise, especially across the Sahel. Warming causes the
number of animals to fall but especially beef cattle. Finally warming causes the net revenue from
all animals to fall, but especially from beef cattle. Increasing precipitation causes the probability
of choosing beef cattle, dairy cattle and sheep to fall and that of goats and chickens to increase.
Wetter climatic conditions reduce the desired number and net revenue of beef cattle, dairy cattle,
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sheep and chickens, but not goats. This effect is most likely due to the change in landscape,
associated with more precipitation, from savanna to forest. Combining all these changes, a 2.5°C
warming results in a 32% loss in expected net income and a 5°C warming leads to a 70% loss in
expected net income. Increasing precipitation by 15% results in a 1% loss in expected net
income.
We also examine climate change impacts using the separate regressions for small and large
livestock farms. With warming, small farms are expected to shift away from dairy cattle and
chickens to goats and sheep. Net incomes will fall for all animals except for sheep. The number
of animals will also fall. Expected income will fall by 13% with a warming of 2.5°C, but recover
with more warming to current levels of income. A 15% decrease in precipitation is expected to
increase small livestock farm incomes by 6%. For large farms, warming will cause a shift to
dairy cattle and sheep and away from goats, chickens and especially beef cattle. The income per
animal falls for all species as temperatures rise. With higher temperatures, large farms choose to
have fewer beef, chickens and sheep and choose more goats and dairy cattle. Large farmers'
incomes are expected to fall by an average of 26% with a 2.5°C warming and by 67% with a 5°C
warming, but a 15% decrease in precipitation is expected to increase these farmers' incomes by
2%.
The study also examines the consequences of a range of climate predictions from three
Atmospheric Oceanic General Circulation Models (AOGCMs). These models predict that
climate change will cause beef cattle to decrease in Africa and sheep and goats to increase. In
general, the climate models predict that the overall number of animals will fall although the
number of goats may increase. They also predict that the net revenue per animal will fall.
Combining all of these effects, the climate models predict average losses of 22% ($8 to $23
billion) in expected net income from livestock by 2020. These damages increase to 31% ($9 to
$24 billion) by 2060, and to 54% ($25 to $40 billion) by 2100.
Examining the effect on small and large farms reveals that small farms will choose dairy cattle
and sheep more often and goats and chickens less often as the primary animal. The income per
animal will tend to fall over time except for sheep. The number of animals will tend to fall with
warming with a few exceptions. The changes in the number of goats and sheep are relatively
negligible. The expected income for small farms will tend to increase over time with the
Canadian Climate Center (CCC) scenarios (34%), but fluctuate with the Parallel Climate Model
(PCM) and Center for Climate System Research (CCSR) scenarios depending on precipitation.
Large farmers, in contrast, will shift away from beef cattle and chickens in favor of dairy cattle,
sheep and goats. Net revenues will fall across animals, but especially for beef cattle. The
numbers of beef cattle and chickens will fall by large amounts, but the numbers of goats and
sheep will increase depending upon the scenarios. Putting all these results together, CCC will
lead to a $6000 reduction in expected net revenue per large farm (77%), CCSR to a $2,700
reduction (34%), and PCM to a $3,400 reduction (43%) by 2100.
The results indicate that warming will be harmful to commercial livestock owners, especially
cattle owners. Owners of commercial livestock farms have few alternatives either in crops or
other animal species. In contrast, small livestock farms are better able to adapt to warming or
precipitation increases by switching to heat tolerant animals or crops. Livestock operations will
be a safety valve for small farmers if warming or drought causes their crops to fail.
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TABLE OF CONTENTS
Section Page
1 Introduction 5
2 Theory 5
3 Data and empirical specification 10
4 Empirical results 10
5 Climate simulations 14
6 Conclusion and policy implications 18
References 20
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1. Introduction
This paper develops a new empirical approach to studying agriculture, the Structural Ricardian
Model, and applies it to studying animal husbandry in Africa. This model, a variation of the
Ricardian approach (Mendelsohn et al. 1994), estimates the underlying profit functions of
specific animals or crops. The original Ricardian model examined the locus of profit maximizing
choices of farmers across all output choices. The Structural Ricardian Model estimates the
farmer's selection of the most profitable species, the number of animals chosen, and the
conditional net revenue per animal. Besides revealing how net revenue changes with climate, this
model also reveals details of how farmers adjust to climate. It explains farmer's choices across
animals (or crops) and measures how sensitive each animal (or crop) is to exogenous variables.
These animal specific results can be more directly compared to natural science based studies
(such as Reilly at al. 1996) and economic production studies of individual crops and animals
(such as Adams et al. 1999).
We use this new methodology to study the impact of climate change on animal husbandry in
Africa. Early analyses of the effects of climate change predicted extensive damage to the
agricultural sector across the globe (Pearce 1996). The bulk of agriculture studies on the effect of
climate change have focused on crops. However, a large fraction of agricultural output is from
livestock. Almost 80% of African agricultural land is used for grazing. African farmers depend
on livestock for income, food, animal products and insurance. Yet there are very few economic
analyses of climatic effects on livestock. An important exception to this gap is the study of the
effects of climate change on American livestock (Adams et al. 1999). American livestock appear
not to be vulnerable to climate change because they live in protected environments (sheds, barns
etc.) and have supplemental feed (e.g. hay and corn). In Africa, by contrast, the bulk of livestock
have no protective structures and they graze off the land. There is every reason to expect that
African livestock will be sensitive to climate change. This study analyzes the behavior of over
9000 African farmers in ten countries in order to measure the climate sensitivity of African
animal husbandry. Of the 9000 farmers interviewed, over 5000 were farming livestock.
The underlying theory of the Structural Ricardian Model is developed in the next section. Section
3 discusses how the data were collected and what variables are available. Section 4 discusses the
estimation procedure and the empirical results. Several climate change scenarios are then
examined in Section 5. The paper looks at both uniform changes in climate across Africa and
climate model predictions. It concludes with a summary of results and policy implications.
2. Theory
A farmer's optimization decision can be seen as a simultaneous multiple-stage procedure. The
farmer chooses the levels of inputs, the desired number of animals and the species that will yield
the highest net profit. Given the profit maximizing inputs from each farmer, one can estimate the
loci of profit maximizing choices for each animal across exogenous environmental factors such
as temperature or precipitation. These are the individual loci that lie beneath the overall profit
function for the farm (Mendelsohn et al. 1994). We call the approach `structural' because it
estimates the underlying profit response functions (the structure) that form the overall Ricardian
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response. For example, in Figure 1 we display a traditional Ricardian response function with
respect to temperature. Underneath the loci of all choices is a set of animal specific response
functions. Given the climate, the farmer must choose the most profitable animal and also the
inputs that will maximize the value of that animal. We examine the individual net revenue
functions for each animal (Structural Ricardian Model) as well as the overall net revenue
function across all animals (Ricardian Model).
We assume that each farmer makes his animal husbandry decisions to maximize profit. Hence,
the probability that an animal is chosen depends on the profitability of that animal or crop. We
assume that farmer i's profit in choosing livestock j (j=1,2,...,J) is
ij =V(K ,Sj) +(K ,Sj) (1)
j j
where K is a vector of exogenous characteristics of the farm and S is a vector of characteristics of
farmer i. For example, K could include climate, soils and access variables and S could include
the age of the farmer and family size. The profit function is composed of two components: the
observable component V and an error term, . The error term is unknown to the researcher, but
may be known to the farmer. The farmer will choose the livestock that gives him the highest
profit. Defining Z = (K,S), the farmer will choose animal j over all other animals k if:
*(Zji) >*(Zki)fork j.[orif (Zki)-(Zji)